Automated tilt compensation in acoustic microscopy

Scanning acoustic microscopy (SAM) is a potent and nondestructive technique capable of producing three-dimensional topographic and tomographic images of specimens. This is achieved by measuring the differences in time of flight (ToF) of acoustic signals emitted from various regions of the sample. The measurement accuracy of SAM strongly depends on the ToF measurement, which is affected by tilt in either the scanning stage or the sample stage. Hence, compensating for the ToF shift resulting from sample tilt is imperative for obtaining precise topographic and tomographic profiles of the samples in a SAM. In the present work, we propose an automated tilt compensation in ToF of acoustic signal based on proposed curve fitting method. Unlike the conventional method, the proposed approach does not demand manually choosing three separate coordinate points from SAM's time domain data. The effectiveness of the proposed curve fitting method is demonstrated by compensating time shifts in ToF data of a coin due to the presence of tilt. The method is implemented for the correction of different amounts of tilt in the coin corresponding to angles 6.67 & DEG;, 12.65 & DEG; and 15.95 & DEG;. It is observed that the present method can perform time offset correction in the time domain data of SAM with an accuracy of 45 arcsec. The experimental results confirm the effectiveness of the suggested tilt compensation technique in SAM, indicating its potential for future applications. Scanning Acoustic Microscopy (SAM) is like a special microscope that helps us look inside objects without breaking them. It does this by using sound waves and measuring how long they take to bounce back. This measurement helps us make 3D images of what is inside. But sometimes, the things we want to look at are not perfectly flat, like a tilted coin. When objects are not perfectly flat and are tilted, the images produced by SAM can contain errors or inaccuracies. These errors can affect the quality and precision of the images, making it important to find ways to correct for these tilt-related issues. So, in this study, we found a way to fix those mistakes automatically. Normally, a person has to choose specific points on the pictures to fix them, but our new way does not need that. We tested our new method on a tilted coin, and it worked well. It made the images more accurate, even when the coin was tilted at different angles. This means that our method can make SAM even better at showing us what is inside things, and it could be really useful in the future.